Selecting diverse product titles to display on a website

ABSTRACT

A system and method for selecting diverse product titles to display on a website are disclosed. In some example embodiments, the methods and systems described herein identify available products to be displayed, cluster the identified products based on their similarity to one another, select one or more products from each of the clusters, and display information, such as a title, associated with the randomly selected products.

CROSS REFERENCE TO RELATED APPLICATIONS

This continuation patent application claims priority to and the benefitof U.S. patent application Ser. No. 15/620,608 filed Jun. 12, 2017 andissued on May 12, 2020 as U.S. Pat. No. 10,650,421, and claims priorityto U.S. patent application Ser. No. 13/490,035, filed Jun. 6, 2012,which issued as U.S. Pat. No. 9,679,316 on Jun. 13, 2017 and claimspriority to U.S. Provisional Patent Application No. 61/493,877, filed onJun. 6, 2011, which are hereby incorporated by reference in theirentirety.

TECHNICAL FIELD

This application relates generally to information retrieval, andspecifically, to a system and method for selecting diverse producttitles given space constraints at runtime.

BACKGROUND

General merchandising of items for sale via a network-basedmerchandising system is well-known. Many websites accessible via theInternet are operated as online stores or auctions. These websitesenable users to purchase items that may be physical items (e.g., anarticle of clothing), electronic data items (e.g., a downloadabledigital media product), or services to be rendered by an affiliatedservice provider. To facilitate potential transactions and therebyimprove user experiences, some websites provide recommendations of itemsto users.

BRIEF DESCRIPTION OF THE DRAWINGS

The present technology is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a network architecture of asystem used to select diverse product titles for display to a clientdevice in some example embodiments.

FIG. 2 is a block diagram illustrating a publication system of availableproducts in some example embodiments.

FIG. 3 is a flow diagram illustrating a method for displayinginformation associated with available products in some exampleembodiments.

FIG. 4 is a flow diagram illustrating a method for generating clustersof similar products in some example embodiments.

FIG. 5 is a flow diagram illustrating a method for selecting availableproducts for display in some example embodiments.

FIG. 6 is a display diagram illustrating displayed informationassociated with selected available products in some example embodiments.

FIG. 7 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions may beexecuted to cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

Overview

Methods and systems to select diverse product titles to display on awebsite are disclosed. In some example embodiments, the methods andsystems described herein identify available products to be displayed,cluster the identified products based on their similarity to oneanother, randomly select one or more products from each of the clusters,and display information, such as a title, associated with the randomlyselected products.

For example, an online auction website may receive a search request fora digital camera, and identify hundreds of cameras that satisfy therequest, many of the identified cameras having titles that are similarto one another. Due to display space constraints, limitations on apresentation area in which to display product descriptions, and/or otherconstraints, the website may not be able to simultaneously present allof the hundreds of cameras in response to the request. Utilizing themethods and systems described herein, the auction website may assigneach of the identified individual cameras to a cluster based on asimilarity of their titles to one another, and then select, in someexample embodiments randomly, one of the cameras from each of theclusters to display via the website. Thus, in some example embodiments,the systems and methods described herein enable an online auctionwebsite or other online retail store to display descriptions for a wideassortment of available products in a given category, as well as providea fair opportunity for descriptions about some or every relevantavailable product to be displayed, among other benefits.

Suitable System

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It may be evident, however, toone skilled in the art that the subject matter of the present disclosuremay be practiced without these specific details.

FIG. 1 is a block diagram illustrating a network architecture of asystem used to select diverse product titles for display to a clientdevice in some example embodiments. For example, the network system 100may be a publication/publisher system 102 where clients may communicateand exchange data within the network system 100. The data may pertain tovarious functions (e.g., selling and purchasing of items) and aspects(e.g., data describing items listed on the publication/publisher system)associated with the network system 100 and its users. Althoughillustrated herein as a client-server architecture as an example, otherexample embodiments may include other network architectures, such as apeer-to-peer or distributed network environment.

A data exchange platform, in an example form of a network-basedpublisher 102, may provide server-side functionality, via a network 104(e.g., the Internet) to one or more clients. The one or more clients mayinclude users that utilize the network system 100 and more specifically,the network-based publisher 102, to exchange data over the network 114.These transactions may include transmitting, receiving (communicating)and processing data to, from, and regarding content and users of thenetwork system 100. The data may include, but are not limited to,content and user data such as feedback data; user reputation values;user profiles; user attributes; product and service reviews; product,service, manufacture, and vendor recommendations and identifiers;product and service listings associated with buyers and sellers; auctionbids; and transaction data, among other things.

In various embodiments, the data exchanges within the network system 100may be dependent upon user-selected functions available through one ormore client or user interfaces (UIs). The UIs may be associated with aclient machine, such as a client machine 106 using a web client 110. Theweb client 110 may be in communication with the network-based publisher102 via a web server 120. The UIs may also be associated with a clientmachine 108 using a programmatic client 112, such as a clientapplication, or a third party server 114 hosting a third partyapplication 116. It can be appreciated in various embodiments the clientmachine 106, 108, or third party application 114 may be associated witha buyer, a seller, a third party electronic commerce platform, a paymentservice provider, or a shipping service provider, each in communicationwith the network-based publisher 102 and optionally each other. Thebuyers and sellers may be any one of individuals, merchants, or serviceproviders, among other things.

Turning to the network-based publisher 102, an application programinterface (API) server 118 and a web server 120 are coupled to, andprovide programmatic and web interfaces respectively to, one or moreapplication servers 122. The application servers 122 host one or morepublication application (s) 124. The application servers 122 are, inturn, shown to be coupled to one or more database server(s) 126 thatfacilitate access to one or more database(s) 128.

In some example embodiments, the web server 120 and the API server 118communicate and receive data pertaining to listings, transactions, andfeedback, among other things, via various user input tools. For example,the web server 120 may send and receive data to and from a toolbar orwebpage on a browser application (e.g., web client 110) operating on aclient machine (e.g., client machine 106). The API server 118 may sendand receive data to and from an application (e.g., client application112 or third party application 116) running on another client machine(e.g., client machine 108 or third party server 114).

The publication application(s) 124 may provide a number of publisherfunctions and services (e.g., search, listing, payment, etc.) to usersthat access the network-based publisher 102. For example, thepublication application(s) 124 may provide a number of services andfunctions to users for listing goods and/or services for sale, searchingfor goods and services, facilitating transactions, and reviewing andproviding feedback about transactions and associated users.Additionally, the publication application(s) 124 may track and storedata and metadata relating to listings, transactions, and userinteractions with the network-based publisher 102.

FIG. 1 also illustrates a third party application 116 that may executeon a third party server 114 and may have programmatic access to thenetwork-based publisher 102 via the programmatic interface provided bythe API server 118. For example, the third party application 116 may useinformation retrieved from the network-based publisher 102 to supportone or more features or functions on a website hosted by the thirdparty. The third party website may, for example, provide one or morelisting, feedback, publisher or payment functions that are supported bythe relevant applications of the network-based publisher 102.

While the example network architecture 100 of FIG. 1 employs aclient-server architecture, a skilled artisan will recognize that thepresent disclosure is not limited to such an architecture. The examplenetwork architecture 100 can equally well find application in, forexample, a distributed or peer-to-peer architecture system.

Referring now to FIG. 2, an example block diagram illustrating multiplecomponents that, in some example embodiments, are provided within thepublication system 102 of the networked system 100 is shown. Thepublication system 102 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between the server machines. The multiple components,themselves, are communicatively coupled (e.g., via appropriateinterfaces), either directly or indirectly, to each other and to variousdata sources, to allow information to be passed between the componentsor to allow the components to share and access common data. Furthermore,the components may access the one or more database(s) 128 via the one ormore database servers 126, both shown in FIG. 1.

In some example embodiments, the publication system 102 comprises anetwork-based marketplace and provides a number of publishing, listing,and price-setting mechanisms whereby a seller (e.g., business orconsumer) may list (or publish information concerning) goods or servicesfor sale, a buyer can search for, express interest in, or indicate adesire to purchase such goods or services, and a price can be set for atransaction pertaining to the goods or services. To this end, thepublication system 102 may comprise at least one publication engine 202and one or more selling engines 204. The publication engine 202 maypublish information, such as item listings or product description pages,on the publication system 102. In some example embodiments, the sellingengines 204 may comprise one or more auction engines that supportauction-format listing and price setting mechanisms (e.g., English,Dutch, Chinese, Double, Reverse auctions, and so on). The variousauction engines may also provide a number of features in support ofthese auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A listing engine 206 allows sellers to conveniently author listings ofitems or authors to author publications. In some example embodiments,the listings pertain to goods or services that a user (e.g., a seller)wishes to transact via the publication system 102. Each good or serviceis associated with a particular category. The listing engine 206 mayreceive listing data such as title, description, and aspect name/valuepairs. Furthermore, each listing for a good or service may be assignedan item identifier. In some example embodiments, a user may create alisting that is an advertisement or other form of informationpublication. The listing information may then be stored to one or morestorage devices coupled to the publication system 102 (e.g., databases128). Listings also may comprise product description pages that displaya product and information (e.g., product title, specifications, reviews,and so on) associated with the product. In some example embodiments, theproduct description page may include an aggregation of item listingsthat correspond to the product described on the product descriptionpage.

Searching the network-based publication system 102 is facilitated by asearching engine 208. For example, the searching engine 208 enableskeyword queries of listings published via the publication system 102. Insome example embodiments, the searching engine 208 receives the keywordqueries from a computing device associated with a user and conducts areview of the storage device storing the listing information. The reviewwill enable compilation of a result set of listings that may be sortedand returned to the client device (e.g., client device 106) of the user.The searching engine 308 may record the query (e.g., keywords) and anysubsequent user actions and behaviors (e.g., navigations).

In a further example, a navigation engine 210 allows users to navigatethrough various categories, catalogs, or inventory data structuresaccording to which listings may be classified within the publicationsystem 102. For example, the navigation engine 210 allows a user tosuccessively navigate down a category tree comprising a hierarchy ofcategories (e.g., the category tree structure 100) until a particularset of listing is reached. Various other navigation applications withinthe navigation engine 210 may be provided to supplement the searchingand browsing applications. The navigation engine 210 may record thevarious user actions (e.g., clicks) performed by the user in order tonavigate down the category tree.

In some example embodiments, a clustering engine 212 examines productdescriptions and clusters the product descriptions based on theirsimilarity to one another. The clustering engine 212 may examine thetitles of products (e.g., available products, products that satisfy acriterion, such as a search query, and so on) to determine thesimilarity between the various products described by the listings. Insome example embodiments, the clustering engine 212 may calculate acosine similarity score for a first product title compared to a secondproduct title. Cosine similarity scores may be calculated for each setof two product titles until the relative similarities of all producttitles have been compared. Product titles having a similarity scoreabove and/or within a predetermined threshold may be clustered together,thereby resulting in one or more product clusters of similar productdescriptions. Example operations performed by the clustering engine 212are discussed in further detail herein.

In some example embodiments, the clustering engine 212 may include oneor more modules and/or components to perform one or more operations ofthe clustering engine 212. The modules may be hardware, software, or acombination of hardware and software. The modules may be executed by oneor more processors. For example, the clustering engine 212 may have asimilarity metric module 220, and an assignment module 222.

The similarity metric module 220 may parse and tokenize the title of theproduct such that each word in the title of the product becomes aseparate token and generate a similarity metric or score between twoproduct descriptions based on the performance of one or more similarityalgorithms on the tokens. For example, the similarity metric module 220may execute a cosine similarity algorithm on the product title tokens todetermine a degree of similarity between two products.

The assignment module 222 may sort combinations (e.g., two or moreproduct descriptions) by similarity scores or other metrics, groupproduct descriptions within one or more clusters, and/or assign productlistings to certain clusters, based on whether the similarity score ofthe product combination meets or exceeds a predetermined threshold.

In some example embodiments, a product selection engine 214 may selectone or more product descriptions from one or more product clusters forpresentation to a client device. In some example embodiments, a portionof a display environment (e.g., a portion of a user interface) on aclient device may be designated for presenting descriptions of relatedor recommended products. The portion of the display environment may belimited based on the design of the web page or based on client devicespecifications (e.g., small display screen size on a mobile device). Asa result, to present a diverse selection of products to a user via aclient device, the product selection engine 214 may select, randomly orotherwise, one or more products from one or more product similarityclusters, depending on space constraints, for presentation. For example,if a user searches for “mp3 players,” the user may not want to see fivevariations of the same product as “recommended” or “related” products.The clustering engine 212 may cluster the five variations of the sameproduct as one product cluster, such that when the time to presentrelated or recommended products arrives, the product selection engine214 may select one or two of the five variations of the same product forpresentation and one or more other products for presentation. Furtherdetails regarding operations performed by the product selection engine214 are discussed in further detail herein.

In some example embodiments, the product selection engine 214 mayinclude one or more modules and/or components to perform one or moreoperations of the product selection engine 214. The modules may behardware, software, or a combination of hardware and software. Themodules may be executed by one or more processors. For example, theproduct selection engine 214 may have a bucket module 230 and aselection module 232. The bucket module 230 may determine from how manyclusters products should be selected for a particular productpresentation. For example, product description display constraints maydictate the number of related or recommended products that may bedisplayed on a client device. The constraints may in turn influence thenumber of buckets from which products to be displayed are selected. Forexample, if four products may be displayed in a certain area of adisplay environment, the bucket module may organize product clustersinto two buckets. Each bucket may include one or more product clusters.The selection module 232 may select products from each bucket forpresentation. In some example embodiments, the selection may beperformed using a technique that randomly selects one of the camerasfrom each of the clusters, in order to maintain eligibility of some orall of the available products for selection and display.

Although the various components of the publication system 102 have beendiscussed in terms of a variety of individual modules and engines, askilled artisan will recognize that many of the components can becombined or organized in other ways. Furthermore, not all components ofthe publication system 102 have been included in FIG. 2. In general,components, protocols, structures, and techniques not directly relatedto functions of example embodiments (e.g., dispute resolution engine,loyalty promotion engine, reputation engines, listing managementengines, account engine) have not been shown or discussed in detail. Thedescription given herein simply provides a variety of exampleembodiments to aid the reader in an understanding of the systems andmethods used herein.

Selecting Diverse Products to Display

As described herein, in some example embodiments, the system may displaya diverse group of products based on generating similarity clusters ofsimilar products and randomly selecting one or more products from eachof the clusters for display. FIG. 3 is a flow diagram illustrating amethod 300 for displaying information associated with available productsin some example embodiments.

In step 310, the method receives a request to display productinformation. For example, the method, via the searching engine 208,receives a request to display information associated with availableproducts that satisfy a search query or other request. The method, insome example embodiments, may consider various different received inputsas a request to display product information, including input associatedwith received search requests, input associated with requests foradditional information, input associated with requests to filterpresented information, input associated with resizing or otherwiseadjusting a display of product information, input associated with arequest to show specific products, input associated with a request toshow alternative products, a launching or navigation to a website orwebpage of a website, and so on.

In step 320, the method identifies product descriptions that satisfy therequest. For example, the method, via the searching engine 208, returnsresults from a received search request, such as a group of availableproducts that satisfy the request. The method, in some exampleembodiments, may determine that a product or products satisfy a requestwhen a title or other information describing the product includes wordsand/or phrases that satisfy the request, and return a group of allavailable products in response to the request.

In step 330, the method groups the identified products into similarityclusters. For example, the method, via the clustering engine 212,assigns each of the identified products to a cluster of products basedon the similarity of their descriptions to one another. In clustering orotherwise grouping products with one another, the method, in someexample embodiments, may utilize a variety of different clusteringtechniques, such as techniques described with respect to FIG. 4.

FIG. 4 is a flow diagram illustrating a method 400 for generatingclusters of similar products in some example embodiments. In step 410,the method generates a single list of distinct words found in titles ofavailable, or identified, products. In step 420, the method generates afrequency vector for each title associated with the available products.In step 430, for every title, the method determines a cosine similaritybetween the title and every other title.

The following is an example calculation of a cosine similarity betweentwo products, or, between the titles of two products, “iPod Nano black16 gb” (title 1), and “iPod Nano 8 gb silver iPod” (title 2). Followingthe method 400, a frequency vector for each of the titles is determined:

Word iPod Nano black 16 gb 8 gb silver Title 1 1 1 1 1 0 0 Title 2 2 1 00 1 1

The method 400 may then calculate the cosine similarity using the twofrequency vectors, as cosine similarity=(vector dot product of vector 1and vector 2)/(square root of dot product of vector 1 anditself)*(square root of dot product of vector 2 and itself)=0.56.

After determining the cosine similarities, the method 400, in step 440,orders the titles from highest to lowest scores for the determinedcosine similarities. In step 450, the method assigns titles havingcosine similarity scores above a predetermined threshold value to acertain cluster. In some examples, the method may utilize a single linkhierarchical clustering algorithm, or other similar clusteringalgorithm, when assigning titles to clusters. In some exampleembodiments, the method may utilize a predetermined value for thethreshold value, such as 0.60, 0.65, 0.70, and so on. In some exampleembodiments, the method may dynamically determine a threshold valuebased on a variety of factors, such as a number of available products, anumber of products to be displayed, and so on.

Thus, in some example embodiments, the method of FIG. 4, given an inputof n <titles>, outputs a list of n<titles>, each having an assignedcluster ID. Turning back to FIG. 3, the method 300, in step 340, selectsa product from each similarity cluster. For example, the method, via theproduct selection engine 214, may perform a variety of differentselection techniques to select a product from each cluster. In someexample embodiments, the method may randomly select one or more productsfrom each of the similarity clusters.

In some example embodiments, the method, in step 340, may generate anddisperse and/or allot the products assigned to the clusters to one ormore buckets to be displayed. FIG. 5 is a flow diagram illustrating amethod 500 for selecting available products for display in some exampleembodiments.

In step 510, the method determines a number of products to display. Insome example embodiments, the method may utilize a predetermined numberof products to display, such as four products, five products, and so on.In some example embodiments, the method may dynamically determine anumber of products to display, such as a number based on an availabledisplay area, based on a number of available products, and so on.

In step 520, the method generates a number of product buckets based onthe determined number of products to display. For example, the methodmay receive information indicating there are 40 available products to bedisplayed, and the four of the products are to be displayed, and maygenerate 10 product buckets based on the information.

In step 530, the method, for each cluster, distributes, allocates and/orassigns at least one product assigned to the cluster to each of thegenerated product buckets. In other words, the method distributes aproduct from each cluster to a distinguished bucket of the generatedbuckets. For example, suppose three clusters (A, B, and C), each havingfour assigned products (A1-A4, B1-B4, C1-C4) are to be dispersed to twobuckets (1 and 2). Following step 530, the method disperses the productsas shown in the following table:

Bucket 1 Bucket 2 A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4

Thus, when the products of all clusters A-C have been dispersed intobuckets, Bucket 1 includes products A1, A3, B1, B3, C1, and C3, andBucket 2 includes A2, A4, B2, B4, C2, and C4.

In step 540, the method selects a bucket of products to display using arandom selection technique. For example, the method may utilize a randomnumber generator to select one of the buckets, Bucket 1 or Bucket 2, inorder to display the products associated with a selected bucket.

Thus, in some example embodiments, the method 300, via step 340,utilizes various algorithms to randomly select one or more products fromeach of the similarity clusters of products.

The following example illustrates a selection algorithm that may beutilized in some example embodiments. The selection algorithm, or logic,may facilitate a fair chance to all available products that may bedisplayed while maintaining diversity in the types of products that aredisplayed, among other benefits. Given an input of 37 available products(n=37), with 5 products to be displayed (m=5), the method may create kbuckets, where k=n/m, and k=k+1 when n % m>0, giving k=8. With A beingequal to an array of k flags to store to the bucket of a currentlydispersed product, you can initialize A to zeros. For each group in 0 toL, the following algorithm may be run:

Do For each product p in group  i. Do 1. int assigned = 0; a. While(assigned == 0)  i. Generate random number from 1 to k ii. If A[k] != 1then assign pto kth bucket and set A[k] = 1; assigned = 1; b. If all theflags in A[k] are 1, then the reset all the flags to 0. (this means wehave assigned at least one product toeach bucket randomly) ii. Done DoneGenerate random number from 1 to k and select kth bucket.

Turning back to FIG. 3, the method 300, in step 350, displays productinformation associated with the selected products. For example, themethod, via the listing engine 206, may display information, such astitles, descriptions, and so on, associated with products selected instep 340 or via method 500, among other techniques. FIG. 6 is a displaydiagram 600 illustrating displayed information associated with selectedavailable products in some example embodiments.

The display diagram 600 depicts an example display of information602-610 as a “Popular Products” sidebar of a webpage that displaysproducts available in auctions or otherwise for purchase via thewebpage. The display diagram 600 utilizes the systems and methodsdescribed herein to present information from a variety of differentproducts that are associated with a search for “video game consoles”received via a search component of the webpage. For example, the displaydiagram 600 presents two different titles 602, 604 for a SonyPlaystation, two different titles 606, 608 for a Microsoft Xbox, and atitle 610 for a Nintendo DS, among other things. The display diagram 600may also display information 615 associated with displaying otherproducts, such as a selectable phrase to find “other popular products.”In some example embodiments, selection of the phrase 615 may triggersome or all of the method described herein, causing the display 600 topresent an updated, randomly generated list of diverse titles, amongother things.

Thus, in some example embodiments, the systems and methods descriedherein enable presentation of a limited, yet diverse, set of titlesassociated with many products that satisfy a search query, such as aquery for “video game consoles.”

Of course, a skilled artisan will appreciate that information may bedisplayed in a variety of different locations on a webpage, such as awebpage for an auction site, an online retailer, and so on, such as amain listings section, a sidebar displaying recommended or preferredproducts, among other things.

CONCLUSION

FIG. 7 shows a diagrammatic representation of machine in the exemplaryform of a computer system 700 within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server or a client machine in server-clientnetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment. The machine may be a server computer,a client computer, a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The example computer system 700 includes a processor 702 (e.g., acentral processing unit (CPU) a graphics processing unit (GPU) or both),a main memory 704 and a static memory 706, which communicate with eachother via a bus 708. The computer system 700 may further include a videodisplay unit 710 (e.g., a liquid crystal display (LCD) or a cathode raytube (CRT)). The computer system 700 also includes an alphanumeric inputdevice 712 (e.g., a keyboard), a cursor control device 714 (e.g., amouse), a disk drive unit 716, a signal generation device 718 (e.g., aspeaker) and a network interface device 720.

The disk drive unit 716 includes a machine-readable medium 722 on whichis stored one or more sets of instructions (e.g., software 724)embodying any one or more of the methodologies or functions describedherein. The software 724 may also reside, completely or at leastpartially, within the main memory 704 and/or within the processor 702during execution thereof by the computer system 700, the main memory 704and the processor 702 also constituting machine-readable media. Thesoftware 724 may further be transmitted or received over a network 726via the network interface device 720.

While the machine-readable medium 722 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical and magnetic media, andcarrier wave signals.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A “hardware module” is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware module at one instance of time and to constitute adifferent hardware module at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

Thus, a method and system to select diverse product titles given spaceconstraints at runtime has been described. Although the presentdisclosure has been described with reference to specific exemplaryembodiments, it may be evident that various modifications and changesmay be made to these embodiments without departing from the broaderspirit and scope of the disclosure. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense.

The accompanying drawings that form a part hereof, show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The preceding technical disclosure is intended to be illustrative, andnot restrictive. For example, the above-described embodiments (or one ormore aspects thereof) may be used in combination with each other. Otherembodiments will be apparent to those of skill in the art upon reviewingthe above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive or, such that “A or B” includes“A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.Furthermore, all publications, patents, and patent documents referred toin this document are incorporated by reference herein in their entirety,as though individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

What is claimed is:
 1. A system, comprising: a non-transitory memorystoring instructions; and a processor configured to execute theinstructions to cause the system to: group a plurality of availableproducts into a plurality of clusters; determine a number of products,from the plurality of available products, to display on a client device;generate a set of buckets to which to allocate the plurality ofavailable products, wherein a particular number of buckets in the set ofbuckets is based on the number of products to display on the clientdevice; allocate individual ones of the plurality of available products,from the plurality of clusters, to the set of buckets, wherein, for afirst cluster of the plurality of clusters, the allocating includesallocating at least one product from the first cluster to each of theset of buckets; and select a particular bucket from the set of buckets.2. The system of claim 1, wherein the particular bucket is selectedusing a random selection technique.
 3. The system of claim 1, whereingrouping the plurality of available products into the plurality ofclusters is performed based on similarity scores indicative of asimilarity between the plurality of available products.
 4. The system ofclaim 1, wherein executing the instructions further causes the systemto: transmit, to the client device, information corresponding to one ormore items allocated to the particular bucket.
 5. The system of claim 1,wherein grouping the plurality of available products into the pluralityof clusters is performed based on lists of descriptive terms associatedwith plurality of available products.
 6. The system of claim 5, whereinthe grouping the plurality of available products into the plurality ofclusters includes: generating frequency vectors using the lists ofdescriptive terms; and determining similarity scores indicative of asimilarity between the plurality of available products using thefrequency vectors.
 7. A method, comprising: grouping, by a computersystem, a plurality of available products into a plurality of clusters;determining, by the computer system, a number of products, from theplurality of available products, to display on a client device;generating, by the computer system, a set of buckets to which toallocate the plurality of available products, wherein a particularnumber of buckets in the set of buckets is based on the number ofproducts to display on the client device; allocating, by the computersystem, individual ones of the plurality of available products, from theplurality of clusters, to the set of buckets, wherein, for a firstcluster of the plurality of clusters, the allocating includes allocatingat least one product from the first cluster to each of the set ofbuckets; and selecting, by the computer system, a particular bucket fromthe set of buckets.
 8. The method of claim 7, wherein the particularbucket is selected using a random selection technique.
 9. The method ofclaim 7, wherein the grouping the plurality of available products intothe plurality of clusters is performed based on similarity scoresindicative of a similarity between the plurality of available products.10. The method of claim 7, further comprising: transmitting, to theclient device, information corresponding to one or more items allocatedto the particular bucket.
 11. The method of claim 7, wherein thegrouping the plurality of available products into the plurality ofclusters is performed based on lists of descriptive terms associatedwith the plurality of available products.
 12. The method of claim 11,wherein the grouping the plurality of available products into theplurality of clusters includes: generating frequency vectors using thelists of descriptive terms; and determining similarity scores indicativeof a similarity between the plurality of available products using thefrequency vectors.
 13. A non-transitory machine-readable medium havinginstructions stored thereon, the instructions executable to causeperformance of operations comprising: grouping a plurality of availableproducts into a plurality of clusters; determining a number of products,from the plurality of available products, to display on a client device;generating a set of buckets to which to allocate the plurality ofavailable products, wherein a particular number of buckets in the set ofbuckets is based on the number of products to display on the clientdevice; allocating individual ones of the plurality of availableproducts, from the plurality of clusters, to the set of buckets,wherein, for a first cluster of the plurality of clusters, theallocating includes allocating at least one product from the firstcluster to each of the set of buckets; and selecting a particular bucketfrom the set of buckets.
 14. The non-transitory machine-readable mediumof claim 13, wherein the particular bucket is selected using a randomselection technique.
 15. The non-transitory machine-readable medium ofclaim 13, wherein the grouping the plurality of available products intothe plurality of clusters is performed based on similarity scoresindicative of a similarity between the plurality of available products.16. The non-transitory machine-readable medium of claim 13, wherein thegrouping the plurality of available products into the plurality ofclusters is performed based on lists of descriptive terms associatedwith the plurality of available products.
 17. The non-transitorymachine-readable medium of claim 13, the operations further comprising:transmitting, to the client device, information corresponding to one ormore items allocated to the particular bucket.
 18. The method of claim7, wherein the selecting the particular bucket is performed subsequentto a request, from the client device, for information associated withproducts that satisfy a search query.
 19. The method of claim 18,further comprising: identifying, by the computer system, the pluralityof available products, from a larger set of products, based on thesearch query specified in the request.
 20. The method of claim 19,wherein the determining the number of products to display on the clientdevice is based on an available display area on a webpage used to sendthe request to the computer system.